Mapping YF Cases

YF <- readRDS("../data_clean/YFcases/YFlong.rds")
brazil <- st_read("../data_clean", "BRAZpolygons")

## Reading layer `BRAZpolygons' from data source `/Users/renikaul/Documents/YF_Brazil/data_clean' using driver `ESRI Shapefile'
## Simple feature collection with 5561 features and 2 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -73.99442 ymin: -33.75206 xmax: -28.83588 ymax: 5.271807
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs

Map of Number of Cases per Muni from 2001-2014

# calculate total cases per muni
totCase <- YF %>%
  group_by(muni.no) %>%
  filter(!is.na(case)) %>%
  summarise(totalCases=sum(case, na.rm=T), totalMonths = n_distinct(month.no))
  

# add to shapefile
brazilYF <- merge(brazil, totCase, by.y="muni.no", by.x = "muni_no", all.x = T)

#create color palette
colpal <- colorBin("YlOrRd", domain = c(1,21), bins = c(1,2,3,4,5,10,15,21))
#create popups
casePop <- paste0(brazilYF$muni_name, 
                      "<br><strong>", brazilYF$totalCases,
                  "</strong> Cases from 2001-2014.")
leaflet(data=brazilYF) %>%
  addPolygons(color = "#444444", weight = 0.1, smoothFactor = 0.5,
    opacity = 1.0, fillOpacity = 0.75,
    fillColor = ~colpal(totalCases),
    highlightOptions = highlightOptions(color = "white", weight = 2,
      bringToFront = TRUE),
    popup=casePop) %>%
  addProviderTiles(providers$CartoDB.Positron)

Map of the Number of Positive Months

For John in Jan 2018, we want a map that has points that denote the number of positive months by their size, with the municipalities colored based on which model they are in (low vs. high NHP richness).

Create map theme:

theme_map <- function(...) {
  theme_minimal() +
  theme(
    axis.line = element_blank(),
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    # panel.grid.minor = element_line(color = "#ebebe5", size = 0.2),
    # panel.grid.major = element_line(color = "#ebebe5", size = 0.2),
    panel.grid.minor = element_blank(),
    plot.background = element_rect(fill = NA, color = NA), 
    panel.background = element_rect(fill = "gray80", color = NA), 
    legend.background = element_rect(fill = NA, color = NA),
    panel.border = element_blank(),
    ...
  )
}

indexSplit <- readRDS("../data_clean/environmental/twoModelSplit.rds")

brazilYF <- merge(brazilYF, indexSplit, by.x = "muni_no", by.y = "muni.no")

brazCnt <- data.frame(st_coordinates(st_centroid(brazilYF)), muni.no = brazilYF$muni_no, totalMonths = brazilYF$totalMonths)

## Warning in st_centroid.sfc(st_geometry(x)): st_centroid does not give
## correct centroids for longitude/latitude data

ggplot() +
  # municipality polygons colored by NHP
  geom_sf(data = brazilYF, aes(fill = above5split), color = NA) +
  scale_fill_manual(values = c("honeydew1", "navajowhite1"), labels = c("High NHP", "Low NHP"), name = "Which primate model?")+
  geom_point(data = brazCnt, aes(size = totalMonths, x= X, y = Y), alpha = 0.5, fill = "dodgerblue", color = "black",pch=21) +
  scale_size_area(name = "Number of months with cases")

## Warning: Removed 5483 rows containing missing values (geom_point).

  theme_map()

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